Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing
Multi-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment’s energy consumption. An MEC system that can efficiently supports both the ultra-Reliable Low Latency Communication (uRLLC) and...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10988620/ |
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| author | Akinbode A. Olawole Fambirai Takawira Chabalala S. Chabalala |
| author_facet | Akinbode A. Olawole Fambirai Takawira Chabalala S. Chabalala |
| author_sort | Akinbode A. Olawole |
| collection | DOAJ |
| description | Multi-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment’s energy consumption. An MEC system that can efficiently supports both the ultra-Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB) services is crucial in providing a diverse and efficient communication for various Internet of Things (IoT) applications. The current MEC models in literature are either deterministic or based on average-based metric hence, not suitable in a practical scenario where task offloading and computation activities are stochastic processes and, the wireless channel is often not interference-free. In this paper, we study the joint task offloading and computation in a mixed traffic of two 5G-based MEC. We consider user equipment (UE) that are cognitive radio enabled and, whose performance are energy constrained. In view of this, energy efficient MEC is formulated as a stochastic optimization with long-term objective while, taking into consideration the tail distribution of the eMBB queue length. The target is to minimize energy consumption and, maximize the achievable data rate subject to probabilistic and statistical constraint on the eMBB task length based on Extreme Value Theorem (EVT), uRLLC reliability and, system capacity. The performance of the proposed MEC model is studied in terms of the latency, energy consumption, user density, and reliability. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed model over the existing model. |
| format | Article |
| id | doaj-art-4c3a7802fb12420b8f18103a90c8bd7f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-4c3a7802fb12420b8f18103a90c8bd7f2025-08-20T01:50:29ZengIEEEIEEE Access2169-35362025-01-0113798717989310.1109/ACCESS.2025.356713910988620Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge ComputingAkinbode A. Olawole0https://orcid.org/0000-0001-5043-7997Fambirai Takawira1https://orcid.org/0000-0002-1975-3497Chabalala S. Chabalala2Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, NigeriaSchool of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South AfricaSchool of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South AfricaMulti-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment’s energy consumption. An MEC system that can efficiently supports both the ultra-Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB) services is crucial in providing a diverse and efficient communication for various Internet of Things (IoT) applications. The current MEC models in literature are either deterministic or based on average-based metric hence, not suitable in a practical scenario where task offloading and computation activities are stochastic processes and, the wireless channel is often not interference-free. In this paper, we study the joint task offloading and computation in a mixed traffic of two 5G-based MEC. We consider user equipment (UE) that are cognitive radio enabled and, whose performance are energy constrained. In view of this, energy efficient MEC is formulated as a stochastic optimization with long-term objective while, taking into consideration the tail distribution of the eMBB queue length. The target is to minimize energy consumption and, maximize the achievable data rate subject to probabilistic and statistical constraint on the eMBB task length based on Extreme Value Theorem (EVT), uRLLC reliability and, system capacity. The performance of the proposed MEC model is studied in terms of the latency, energy consumption, user density, and reliability. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed model over the existing model.https://ieeexplore.ieee.org/document/10988620/Computational task offloadingenhanced mobile broadband (eMBB)fifth generation (5G)multi-user edge computing (MEC)stochastic processesultra-reliable low latency communications (uRLLC) |
| spellingShingle | Akinbode A. Olawole Fambirai Takawira Chabalala S. Chabalala Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing IEEE Access Computational task offloading enhanced mobile broadband (eMBB) fifth generation (5G) multi-user edge computing (MEC) stochastic processes ultra-reliable low latency communications (uRLLC) |
| title | Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing |
| title_full | Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing |
| title_fullStr | Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing |
| title_full_unstemmed | Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing |
| title_short | Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing |
| title_sort | task offloading and computation in hybrid urllc and embb cognitive radio enabled 5g based multiuser edge computing |
| topic | Computational task offloading enhanced mobile broadband (eMBB) fifth generation (5G) multi-user edge computing (MEC) stochastic processes ultra-reliable low latency communications (uRLLC) |
| url | https://ieeexplore.ieee.org/document/10988620/ |
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